Neerja Doshi

Data scientist at Amazon Web Services

[Neerja Doshi @DoshiNeerja]

@BBOYWORTHING @billburr Has this begun yet (if you still have your phone)? Stuck in loads of traffic!

[Neerja Doshi @DoshiNeerja]

@aaronbronow @khushbo92795036 @LithiumFilms @wsdot Thank you for the guidance!

[Neerja Doshi @DoshiNeerja]

@aaronbronow @khushbo92795036 @LithiumFilms @wsdot At this point is it advisable to go through US2 westbound or should we wait for snoqualmie to open. Currently stuck near ellensburg

[Yann LeCun @ylecun]

Slides of the Holst Memorial Lecture I gave at TU Eindhoven on Nov 29, 2018.

https://t.co/AHTLGdy5wR

[Kerem Turgutlu @KeremTurgutlu]

14th place in Airbus Ship Detection Challenge with @fastdotai (both v0.7 and v1.0), many thanks to @robgardin @radekosmulski. It was a pleasure teaming up with you guys !

[Amazon News @amazonnews]

In case you were wondering, Morticia the #CorpseFlower isn’t smelling...yet. The horticulturalists are expecting a full-on fragrance later tonight.

[Ben Hamner @benhamner]

Kaggle Kernels can be a great tool for package documentation - you get interactive executable & reproducible tutorials with a single click

Ex:
- Nice overview basic pandas functionality: https://t.co/1qfF2mE3bx
- Pandas tutorial on Pokemon data https://t.co/BYtUFlYqVC

[Prince Grover @groverpr4]

My effort to summarise applications of matrix decomposition techniques taught by @math_rachel in Computational Linear Algebra class at USF. https://t.co/ZOpD0Ed6Cm

[Neerja Doshi @DoshiNeerja]

Sanitary napkins exempted from GST https://t.co/SSO7uKo1Aa via @timesofindia

[Santhosh Hari @srisanthosh]

My recent article explaining Locality Sensitive Hashing (LSH) and how it can be used to efficiently search in high dimensional spaces.

https://t.co/mbErzw73nT

[Terence Parr @the_antlr_guy]

Nice article by @usfca_msds grad student @shik1470 on locality sensitive hashing for dimensionality reduction and finding nearest neighbors. https://t.co/ZFAtaRiddJ

[KDnuggets @kdnuggets]

#DeepLearning Best Practices –  Weight Initialization https://t.co/madQAlNTvn

[Nathan Yau @flowingdata]

I analyzed 100k happy moments to find out what makes people the most happy, and it made me happy https://t.co/gZ1RUTk5KW

[Terence Parr @the_antlr_guy]

Launched a new website, https://t.co/WDCq9K3lwv, dedicated to giving deep but intuitive explanations of machine learning and related topics.

[Fritz AI @fritzlabs]

New Heartbeat contributor post! @DoshiNeerja explores Recommendation Systems, with detailed looks at methods for implementing them and ways to evaluate their effectiveness. https://t.co/OguaibirTh

[Neerja Doshi @DoshiNeerja]

To understand the methods used to build recommendations systems and the metrics for evaluating their effectiveness, head over to my latest blog post!

https://t.co/yL4J5rRJZP
#DataScience #recommendations #MachineLearning #DeepLearning

[Fritz AI @fritzlabs]

Contributor @shik1470 explores the key validation strategies for machine learning models in his excellent new Heartbeat post: https://t.co/mtPJFlFJRS

[Prince Grover @groverpr4]

Want to gain intuition for various loss functions used to train regression models? Checkout my blog and it's not only MSE and MAE.

Still not sure why Huber, log-cosh or quantile losses are not much used or talked about, given their pros over MSE/MAE.

https://t.co/vczNSPlEpl

[Neerja Doshi @DoshiNeerja]

Awesome notebook for understanding data structures and algorithms, and how they can be implemented in Python! Great work!! https://t.co/tbUDvyxOZ3

[Shikhar Gupta @shik1470]

For all the folks who are preparing for data science interviews like me, here is a notebook on the implementation of common data structures and algorithms that might be useful.
NBviewer: https://t.co/QBGzxfPQTI
Github: https://t.co/85t1jNV7Js